2026-06-29T09:25:28Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/998402025-12-19T14:48:34Zcom_20.500.14352_14col_20.500.14352_15

A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers Hernández Lorenzo, Laura Gil‐Moreno, Maria José Ortega‐Madueño, Isabel Cárdenas Fernández, María Cruz Diez‐Cirarda, María Delgado Álvarez, Alfonso Palacios‐Sarmiento, Marta Matías-Guiu Guía, Jorge Corrochano Sánchez, Silvia Ayala Rodrigo, José Luis Matias-Guiu Antem, Jordi 616.894-053.9 Alzheimer's disease Cerebrospinal fluid Clustering analysis Early detection Machine learning Mild cognitive impairment Ciencias Biomédicas 32 Ciencias Médicas Aims: The AT(N) classification system not only improved the biological characteriza-tion of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values. Methods: We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1– 42), Aβ(1– 42 ) /Aβ(1– 40) ratio, tTau, and pTau. Results: The optimal solution yielded three clusters in both cohorts, significantly dif-ferent in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impair-ment subjects with faster progression to dementia. Conclusion: We propose this data-driven three- group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, com-plementary to the AT(N) system classification. Universidad Complutense de Madrid Instituto de Salud Carlos III Ministerio de Ciencia, Innovación y Universidades (España) Depto. de Psicobiología y Metodología en Ciencias del Comportamiento Fac. de Psicología TRUE pub 2024-02-07T09:59:57Z 2024-02-07T09:59:57Z 2023-07-27 journal article VoR https://hdl.handle.net/20.500.14352/99840 1755-5930 10.1111/cns.14382 1755-5949 eng info:eu-repo/grantAgreement/CT63/19-CT64/19 info:eu-repo/grantAgreement/PID2019-110866RB-I00 info:eu-repo/grantAgreement/CD22/00043 Hernández‐Lorenzo, L., Gil‐Moreno, M. J., Ortega‐Madueño, I., Cárdenas, M. C., Diez‐Cirarda, M., Delgado‐Álvarez, A., Palacios‐Sarmiento, M., Matias‐Guiu, J., Corrochano, S., Ayala, J. L., Matias‐Guiu, J. A., & for the Alzheimer’s Disease Neuroimaging Initiative. (2024). A data‐driven approach to complement the A/T/(N) classification system using CSF biomarkers. CNS Neuroscience & Therapeutics, 30(2), e14382. https://doi.org/10.1111/cns.14382 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ open access application/pdf Wiley Open Access